“In some cases, the Learn to Grow framework actually got better at performing the old tasks. This is called backward transfer, and occurs when you find that learning a new task makes you better at an old task. We see this in people all the time; not so much with AI,” ungkap Caiming Xiong, Ph.D, direktur riset Salesforce Research dan co-author studi ilmiah itu (U.S. Army Research Laboratory, 20/5/2019).

Hasil riset itu membawa teknologi AI selangkah lebih dekat membantu misi-misi militer secara lebih efektif. “This Army investment extends the current state of the art machine learning techniques that will guide our Army Research Laboratory researchers as they develop robotic applications, such as intelligent maneuver and learning to recognize novel objects. This research brings AI a step closer to providing our warfighters with effective unmanned systems that can be deployed in the field,” ungkap Dr. Mary Anne Fields, manajer program Intelligent Systems pada Army Research Office, satu elemen Army Research Laboratory pada U.S. Army Combat Capabilities Development Command di Amerika Serikat (U.S. Army Research Laboratory, 20/5/2019).

Lebih rinci Dr. Mary Anne Fields menguraikan kebutuhan sistem intelijen dan pelaksanaan misi berbasis AI. “The Army needs to be prepared to fight anywhere in the world so its intelligent systems also need to be prepared. We expect the Army's intelligent systems to continually acquire new skills as they conduct missions on battlefields around the world without forgetting skills that have already been trained. For instance, while conducting an urban operation, a wheeled robot may learn new navigation parameters for dense urban cities, but it still needs to operate efficiently in a previously encountered environment like a forest,” ungkap Dr. Mary Anne Fields, manajer program Intelligent Systems pada Army Research Office, satu elemen Army Research Laboratory pada U.S. Army Combat Capabilities Development Command (U.S. Army Research Laboratory, 20/5/2019).

“Deep neural network AI systems are designed for learning narrow tasks. As a result, one of several things can happen when learning new tasks, systems can forget old tasks when learning new ones, which is called catastrophic forgetting. Systems can forget some of the things they knew about old tasks, while not learning to do new ones as well. Or systems can fix old tasks in place while adding new tasks -- which limits improvement and quickly leads to an AI system that is too large to operate efficiently. Continual learning, also called lifelong-learning or learning-to-learn, is trying to address the issue,” ungkap Xilai Li, co-lead author karya ilmiah itu dan kandidat doktor pada North Carolina State University (NC State) (U.S. Army Research Laboratory, 20/5/2019).

“We've run experiments using several datasets, and what we've found is that the more similar a new task is to previous tasks, the more overlap there is in terms of the existing layers that are kept to perform the new task. What is more interesting is that, with the optimized -- or “learned” topology -- a network trained to perform new tasks forgets very little of what it needed to perform the older tasks, even if the older tasks were not similar,” Xilai Li, mahasiswa doktoral pada NC State (U.S. Army Research Laboratory, 20/5/2019).